Beschreibung:
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Intro -- Contents -- Preface -- 1 Introduction -- 2 Probability -- 3 Generative Models for Discrete Data -- 4 Gaussian Models -- 5 Bayesian Statistics -- 6 Frequentist Statistics -- 7 Linear Regression -- 8 Logistic Regression -- 9 Generalized Linear Models and the Exponential Family -- 10 Directed Graphical Models (Bayes Nets) -- 11 Mixture Models and the EM Algorithm -- 12 Latent Linear Models -- 13 Sparse Linear Models -- 14 Kernels -- 15 Gaussian Processes -- 16 Adaptive Basis Function Models -- 17 Markov and Hidden Markov Models -- 18 State Space Models -- 19 Undirected Graphical Models (Markov Random Fields) -- 20 Exact Inference for Graphical Models -- 21 Variational Inference -- 22 More Variational Inference -- 23 Monte Carlo Inference -- 24 Markov Chain Monte Carlo (MCMC) Inference -- 25 Clustering -- 26 Graphical Model Structure Learning -- 27 Latent Variable Models for Discrete Data -- 28 Deep Learning -- Notation -- Bibliography -- Index to Code -- Index to Keywords.
Anmerkungen:
Includes bibliographical references and index. - Electronic reproduction; Palo Alto, Calif; ebrary; 2011; Available via World Wide Web; Access may be limited to ebrary affiliated libraries